class_docstr: Log to local or remote file system in `TensorBoard <https://www.tensorflow.org/tensorboard>`_ format.
  
  Implemented using :class:`~tensorboardX.SummaryWriter`. Logs are saved to
  ``os.path.join(save_dir, name, version)``. This is the default logger in Lightning, it comes
  preinstalled.
  
  This logger supports logging to remote filesystems via ``fsspec``. Make sure you have it installed
  and you don't have tensorflow (otherwise it will use tf.io.gfile instead of fsspec).
  
  Example:
  
  .. testcode::
      :skipif: not _TENSORBOARD_AVAILABLE or not _TENSORBOARDX_AVAILABLE
  
      from lightning.pytorch import Trainer
      from lightning.pytorch.loggers import TensorBoardLogger
  
      logger = TensorBoardLogger("tb_logs", name="my_model")
      trainer = Trainer(logger=logger)
  
  Args:
      save_dir: Save directory
      name: Experiment name. Defaults to ``'default'``. If it is the empty string then no per-experiment
          subdirectory is used.
      version: Experiment version. If version is not specified the logger inspects the save
          directory for existing versions, then automatically assigns the next available version.
          If it is a string then it is used as the run-specific subdirectory name,
          otherwise ``'version_${version}'`` is used.
      log_graph: Adds the computational graph to tensorboard. This requires that
          the user has defined the `self.example_input_array` attribute in their
          model.
      default_hp_metric: Enables a placeholder metric with key `hp_metric` when `log_hyperparams` is
          called without a metric (otherwise calls to log_hyperparams without a metric are ignored).
      prefix: A string to put at the beginning of metric keys.
      sub_dir: Sub-directory to group TensorBoard logs. If a sub_dir argument is passed
          then logs are saved in ``/save_dir/name/version/sub_dir/``. Defaults to ``None`` in which
          logs are saved in ``/save_dir/name/version/``.
      \**kwargs: Additional arguments used by :class:`tensorboardX.SummaryWriter` can be passed as keyword
          arguments in this logger. To automatically flush to disk, `max_queue` sets the size
          of the queue for pending logs before flushing. `flush_secs` determines how many seconds
          elapses before flushing.
docstr: Called after model checkpoint callback saves a new checkpoint.
  
  Args:
      checkpoint_callback: the model checkpoint callback instance
docstr_tok: ["Called","after","model","checkpoint","callback","saves","a","new","checkpoint",".","Args",":","checkpoint_callback",":","the","model","checkpoint","callback","instance"]
code: def after_save_checkpoint(
      self, checkpoint_callback: ModelCheckpoint
  ) -> None:
      """Called after model checkpoint callback saves a new checkpoint.
  
      Args:
          checkpoint_callback: the model checkpoint callback instance
  
      """
      pass
  
imports: import logging
  import os
  from argparse import Namespace
  from typing import Any, Dict, Optional, Union
  from torch import Tensor
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.loggers.tensorboard import _TENSORBOARD_AVAILABLE
  from lightning.fabric.loggers.tensorboard import TensorBoardLogger
  from lightning.fabric.utilities.cloud_io import _is_dir
  from lightning.fabric.utilities.logger import _convert_params
  from lightning.fabric.utilities.types import _PATH
  from lightning.pytorch.callbacks import ModelCheckpoint
  from lightning.pytorch.core.saving import save_hparams_to_yaml
  from lightning.pytorch.loggers.logger import Logger
  from lightning.pytorch.utilities.imports import _OMEGACONF_AVAILABLE
  from lightning.pytorch.utilities.rank_zero import rank_zero_only, rank_zero_warn
label_desc: Use image node_id 9 for calling the TensorBoardLogger obj's underlying member method code with example usage: obj.after_save_checkpoint(checkpoint_callback) without return types